/SEFrame

An Efficient and Effective Framework for Session-based Social Recommendation

Primary LanguagePythonMIT LicenseMIT

SEFrame

This repository contains the code for the paper "An Efficient and Effective Framework for Session-based Social Recommendation".

Requirements

  • Python 3.8
  • CUDA 10.2
  • PyTorch 1.7.1
  • DGL 0.5.3
  • NumPy 1.19.2
  • Pandas 1.1.3

Usage

  1. Install all the requirements.

  2. Download the datasets:

  3. Create a folder called datasets and extract the raw data files to the folder.
    The folder should include the following files for each dataset:

    • Gowalla: loc-gowalla_totalCheckins.txt and loc-gowalla_edges.txt
    • Delicious: user_taggedbookmarks-timestamps.dat and user_contacts-timestamps.dat
    • Foursquare: dataset_WWW_Checkins_anonymized.txt and dataset_WWW_friendship_new.txt

  4. Preprocess the datasets using the Python script preprocess.py.
    For example, to preprocess the Gowalla dataset, run the following command:

    python preprocess.py --dataset gowalla

    The above command will create a folder datasets/gowalla to store the preprocessed data files.
    Replace gowalla with delicious or foursquare to preprocess other datasets.

    To see the detailed usage of preprocess.py, run the following command:

    python preprocess.py -h

  5. Train and evaluate a model using the Python script run.py.
    For example, to train and evaluate the model NARM on the Gowalla dataset, run the following command:

    python run.py --model NARM --dataset-dir datasets/gowalla

    Other available models are NextItNet, STAMP, SRGNN, SSRM, SNARM, SNextItNet, SSTAMP, SSRGNN, SSSRM, DGRec, and SERec.
    You can also see all the available models in the srs/models folder.

    To see the detailed usage of run.py, run the following command:

    python run.py -h

Dataset Format

You can train the models using your datasets. Each dataset should contain the following files:

  • stats.txt: A TSV file containing three fields, num_users, num_items, and max_len (the maximum length of sessions). The first row is the header and the second row contains the values.

  • train.txt: A TSV file containing all training sessions, where each session has three fileds, namely, sessionId, userId, and items. Both sessionId and userId should be integers. A session with a larger sessionId means that it was generated later (this requirement can be ignored if the used models do not care about the order of sessions, i.e., when the models are not DGRec). The userId should be in the range of [0, num_users). The items field of each session contains the clicked items in the session which is a sequence of item IDs separated by commas. The item IDs should be in the range of [0, num_items).

  • valid.txt and test.txt: TSV files containing all validation and test sessions, respectively. Both files have the same format as train.txt. Note that the session IDs in valid.txt and test.txt should be larger than those in train.txt.

  • edges.txt: A TSV file containing the relations in the social network. It has two columns, follower and followee. Both columns contain the user IDs.

You can see datasets/delicious for an example of the dataset.

Citation

If you use this code for your research, please cite our paper:

@inproceedings{chen2021seframe,
   title="An Efficient and Effective Framework for Session-based Social Recommendation",
   author="Tianwen {Chen} and Raymond Chi-Wing {Wong}",
   booktitle="Proceedings of the Fourteenth ACM International Conference on Web Search and Data Mining (WSDM '21)",
   pages="400--408",
   year="2021"
}